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Grade Selection and Blending to Optimize Cost and Quality

Author

Listed:
  • Uday S. Karmarkar

    (110 Westwood Plaza, Los Angeles, California 90095-1481)

  • Kumar Rajaram

    (Operations and Technology Management, The Anderson School, University of California, Los Angeles, California 90095)

Abstract

In many chemical process applications, a large mix of products is produced by blending them from a much smaller set of basic grades. The basic grades themselves are typically produced on the same process equipment and inventoried in batches. Decisions that arise in this process include selecting the set of basic grades, determining how much of each basic grade to produce, and how to blend basic grades to meet final product demand. We model this problem as a nonlinear mixed-integer program, which minimizes total grade inclusion, batching, blending, and quality costs subject to meeting quality and demand constraints for these products. Heuristics and lower bounds are developed and tested. The methods are applied to data from Europe's leading manufacturer of wheat- and starch-based products. Our results suggest that this model could potentially reduce annual costs by a minimum of 7%, translates to annual savings of around $5 million.

Suggested Citation

  • Uday S. Karmarkar & Kumar Rajaram, 2001. "Grade Selection and Blending to Optimize Cost and Quality," Operations Research, INFORMS, vol. 49(2), pages 271-280, April.
    • Handle: RePEc:inm:oropre:v:49:y:2001:i:2:p:271-280
    DOI: 10.1287/opre.49.2.271.13533
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    References listed on IDEAS

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    1. Ashayeri, J. & van Eijs, A. G. M. & Nederstigt, P., 1994. "Blending modelling in a process manufacturing: A case study," European Journal of Operational Research, Elsevier, vol. 72(3), pages 460-468, February.
    2. Clarence H. Martin & Sanford L. Lubin, 1985. "Optimization Modeling for Business Planning at Trumbull Asphalt," Interfaces, INFORMS, vol. 15(6), pages 66-72, December.
    3. Billy Rigby & Leon S. Lasdon & Allan D. Waren, 1995. "The Evolution of Texaco’s Blending Systems: From OMEGA to StarBlend," Interfaces, INFORMS, vol. 25(5), pages 64-83, October.
    4. Glen, J. J., 1988. "A mixed integer programming model for fertiliser policy evaluation," European Journal of Operational Research, Elsevier, vol. 35(2), pages 165-171, May.
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    Cited by:

    1. Jeffrey R. Stokes & Peter R. Tozer, 2006. "Optimal Feed Mill Blending," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 28(4), pages 543-552.
    2. Francesco Gaglioppa & Lisa A. Miller & Saif Benjaafar, 2008. "Multitask and Multistage Production Planning and Scheduling for Process Industries," Operations Research, INFORMS, vol. 56(4), pages 1010-1025, August.
    3. Felipe Caro & Kumar Rajaram & Jens Wollenweber, 2012. "Process Location and Product Distribution with Uncertain Yields," Operations Research, INFORMS, vol. 60(5), pages 1050-1063, October.
    4. Benhamou, Latifa & Giard, Vincent & Khouloud, Mehdi & Fenies, Pierres & Fontane, Frédéric, 2020. "Reverse Blending: An economically efficient approach to the challenge of fertilizer mass customization," International Journal of Production Economics, Elsevier, vol. 226(C).
    5. Lingxiu Dong & Panos Kouvelis & Xiaole Wu, 2014. "The Value of Operational Flexibility in the Presence of Input and Output Price Uncertainties with Oil Refining Applications," Management Science, INFORMS, vol. 60(12), pages 2908-2926, December.
    6. Rajaram, Kumar & Robotis, Andreas, 2004. "Analyzing variability in continuous processes," European Journal of Operational Research, Elsevier, vol. 156(2), pages 312-325, July.

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